Paper summarynipsreviewsThis paper presents a method for nonblind deconvolution of blurry images, that also can also fix artifacts (e.g. compression, clipping) in the input, and is robust to deviations from the input generation model. A convolutional network is used both to deblur and fix artifacts; deblurring is performed using a sequence of horizontal and vertical conv kernels, taking advantage of a high degree of separability in the pseudoinverse blur kernel, and are initialized with a decomposition of the pseudoinverse. A standard compact-kernel convnet is stacked on top, allowing further fixing of artifacts and noise, and traned end-to-end with pairs of blurry and ground truth images.

This paper presents a method for nonblind deconvolution of blurry images, that also can also fix artifacts (e.g. compression, clipping) in the input, and is robust to deviations from the input generation model. A convolutional network is used both to deblur and fix artifacts; deblurring is performed using a sequence of horizontal and vertical conv kernels, taking advantage of a high degree of separability in the pseudoinverse blur kernel, and are initialized with a decomposition of the pseudoinverse. A standard compact-kernel convnet is stacked on top, allowing further fixing of artifacts and noise, and traned end-to-end with pairs of blurry and ground truth images.